Nate Kornell’s research shows that difficulty in learning is often a sign that you’re learning. Difficulty is in fact desirable, and the right amount of it is key to effective, long-term learning. This is one of the most fascinating findings in my I learned in college but that I didn’t internalize until 7 years later, after working and learning many new skills.
Often when I’m learning a new skill or put into a new situation on the job, I encounter difficulty and a part of me thinks “this is a sign that I’m not good enough to do this.” The impostor syndrome is a well documented but not well internalized phenomenon that many of us feel, and certainly has something to do with that sentiment.
But in data science in particular and in the modern job market in general, constant learning and re-skilling will be increasingly important. Understanding how to learn effectively, and how to understand your own experience while learning, will be a critical factor for success in this area.
Bjork, Dunlosky, and Kornell make this point clear in their review: “Our complex and rapidly changing world creates a need for self-initiated and self-managed learning—not only during the years typically associated with formal education, but also across the lifespan—and technological advances provide new opportunities for such learning” (Bjork et al 2013).
Just as the importance of self-guided learning increases however, we cannot rely on ourselves for an accurate view of what constitutes learning: “our intuitions and introspections appear to be unreliable as a guide to how we should manage our own learning activities” (Bjork et al 2013).
Another related topic is the idea of specialization versus general exposure. In Range by David Epstein, dispels the common misconception that expertise requires early and constant specialization in a topic, and shows how it is the exception rather than the rule among the successful. The “Tiger Woods approach” of specializing and beginning rigorous training - the best path to success in conventional wisdom - actually is extremely rare and can actually be counterproductive. Conversely, having a broad exposure while you are young and then specializing intensely as you get older is a path shown to create phenomenal successes like Roger Federer in tennis, [TODO: get other examples from Range].
Many times as an (aspiring) data scientist, we can feel overwhelmed by the availability of resources, the sheer breadth of topics, and the constant pace of new knowledge and tools being released. We can interpret struggles to understand new topic areas as a sign of our incompetence rather than a potential sign of effective learning - the learning experience will not always be pleasant, but that difficulty is irreducible insofar as it helps you retain what you learned for longer and comprehends the topic more deeply. Similarly, we may feel our earlier topics expertise or education is less relevant, but we actually the broader exposure will help you to be more creative and connect ideas across disciplines, recognized patterns even in new situations, and ultimately be a more successful and satisfied data scientist.
## Future discussion and reading
Future discussions on this blog will relate these ideas to “imposter’s syndrome” (or alternative framings) and the idea of “learning how to learn.”
Imposter syndrome reading: - https://brohrer.github.io/imposter_syndrome.html - https://caitlinhudon.com/2018/01/19/imposter-syndrome-in-data-science/